Pre-constructed query recommendations for data analytics

ABSTRACT

A process for recommending pre-constructed queries in data analytics includes writing different records to a correlation data structure correlating different data classifications of data to different queries and, subsequent to the writing, establishing a communicative connection by a data analytics application to an underlying database. Thereafter, a data model for data in the database may be constructed in the data analytics application and at least one of the different queries may be selected in the correlation data structure that correlates to the classification of the data in the data model. Finally, the selected one of the different queries may be displayed in the data analytics application to an end user so as to provide an intelligent recommendation for the addition of the selected one of the different queries without requiring the end user to alone and without assistance discover the suitability of the selected one of the different queries.

CROSS REFERENCE TO RELATED APPLICATIONS

This U.S. patent application is a continuation of, and claims priorityunder 35 U.S.C. § 120 from, U.S. patent application Ser. No. 17/066,439,filed on Oct. 8, 2020. The disclosure of this prior application isconsidered part of the disclosure of this application and is herebyincorporated by reference in its entirety

TECHNICAL FIELD

The present disclosure relates to the field of data analytics andbusiness intelligence (BI), and more particularly to the formulation ofqueries for BI in a data analytics system.

BACKGROUND

The term database refers to an organized collection of data, stored andaccessed electronically by way of a computing system. A databasemanagement system (DBMS) in turn is a computer program that provides aninterface between the database and one or more end users so as tofacilitate the interaction by each end user with the database. A DBMSgenerally also provides an interface to other computer programs toaccess the data in the underlying database. Generally speaking, endusers and other computer programs interact with the database through theDBMS using query directives formed in conformance with a correspondingquery language such as the venerable structured query language (SQL).

While the very basic use of SQL to query and manage data in a databaseis of no great difficulty for many end users, formulating more complexSQL queries is not for the faint of heart. More importantly, specifyinga query irrespective of the mechanics of the actual query requires astrong understanding of the data in the database and the underlyingrelationships between the data. To the extent that “reading” the contentof a database is not practical, it is known to model a database so thatthe created database model can then be introspected so as to afford adeeper understanding of the data in the database. Indeed, modern dataanalytics tools permit not only the modeling of an existing database,but also the formulation of SQL queries to be executed against thedatabase based upon knowledge only supplied by the model.

In this regard, a data model is an abstract model that describes how adata set of a database is organized and guides the construction ofqueries with respect to the data of the data set. The data modelgenerally contains a join graph whose vertices each reference a tableand whose edges reflect join conditions between references to thetables. As well, the join graph may also describe the columns in thosetables, columns that are derived from other columns via expressions,collections of columns by which queries are typically sorted,collections of columns by which queries are typically grouped intosub-totals and totals, expressions that are derived by combining columnvalues during the construction of a sub-total or total, and othersuggestions for how queries might be formed on the data.

Despite the robust nature of a data model, the introspection of a datamodel for a database, however, is not alone sufficient to enjoy acomplete understanding of the data in a database. In fact, automateddatabase modeling tools generally only are able to produce a databasemodel explicitly mapping to the underlying database including queriespreviously defined in the model as previously executed against thedatabase through the DBMS for the database. However, so much implicitinformation remains undiscovered that otherwise might be inferred fromthe existing data in the database, but which has not yet been explicitlydefined.

BI is natural consequence of data analytics, in which differentreal-world questions regarding the data collected for a business areanswered utilizing data analytics so as to provide “actionable insights”into the nature, operation and performance of a business. Thesereal-world questions are asked in the form of one or more queries to thedata model that are not already present in the model, but which areinferred from the data model. Modern BI tools not only provide somestock forms of these queries, but also provide a programmatic interfacewhich permits the end user to formulate custom query blocks to answerthose real-world questions of specific interest to the end user. Yet,mastering the skill set necessary to create a block of queriessufficient to achieve the “actionable insights” sought is no small featand often is not feasible for some end users.

SUMMARY

Examples of the present disclosure address deficiencies of the art inrespect to query block customization of a BI tool and provide a noveland non-obvious method, system, and computer program product forrecommending pre-constructed queries in data analytics. One aspect ofthe disclosure provides a method for recommending pre-constructedqueries in data analytics. The method includes writing different recordsto a correlation data structure correlating different dataclassifications of data to different queries. The method also includes,subsequent to the writing, establishing a communicative connection by adata analytics application to an underlying database from over acomputer communications network. The method further includesconstructing in the data analytics application, a data model for data inthe database and classifying the data in the data model. The method alsoincludes selecting in the correlation data structure, at least one ofthe different queries correlated to the classification of the data inthe data model. The method further includes displaying in the dataanalytics application, the selected one of the different queries.

Implementations of the disclosure may include one or more of thefollowing optional features. In some examples, the displaying includesdisplaying a set of directives corresponding to the selected one of thequeries. Here, the directives may be markup language statements.

In some implementations, the selection in the correlation datastructure, includes a selection of at least one of the different queriescorrelated to a combination of classifications of data in the datamodel. In some configurations, the selection in the correlation datastructure includes a selection of a user interface view that is avisualization of a portion of the data model. In some examples, theselection in the correlation data structure includes a selection of areport of data from a portion of the data model. In some configurations,the selection in the correlation data structure includes a selection ofa programmatic directive in a third-party application.

Another aspect of the disclosure provides a data analytics dataprocessing system configured for recommending pre-constructed queries.The system includes a host computing platform including one or morecomputers, each with memory and at least one processor. The system alsoincludes a database storing therein data and a data analytics computerprogram executing in the memory and managing queries against a datamodel modeling the data in the database. The system further includes apre-constructed query recommendation module including computer programinstructions enabled during execution in the memory of the hostcomputing platform to perform operations. One of the operations includeswriting different records to a correlation data structure correlatingdifferent data classifications of data to different queries. Anotheroperation includes, subsequent to the writing, establishing acommunicative connection by a data analytics application to anunderlying database from over a computer communications network. Theoperations also include constructing in the data analytics application,a data model for data in the database and classifying the data in thedata model. Another operation includes selecting in the correlation datastructure, at least one of the different queries correlated to theclassification of the data in the data model. The operations furtherinclude displaying in the data analytics application, the selected oneof the different queries.

Implementations of the disclosure may include one or more of thefollowing optional features. In some examples, the displaying includesdisplaying a set of markup language statements corresponding to theselected one of the queries. In some implementations, the selection inthe correlation data structure includes a selection of at least one ofthe different queries correlated to a combination of classifications ofdata in the data model.

In some examples, the selection in the correlation data structureincludes a selection of a user interface view that is a visualization ofa portion of the data model. Optionally, the selection in thecorrelation data structure includes a selection of a report of data froma portion of the data model. In some configurations, the selection inthe correlation data structure includes a selection of a programmaticdirective in a third-party application.

Another aspect of the disclosure provides a computer program product forrecommending pre-constructed queries in data analytics. The computerprogram product includes a computer readable storage medium havingprogram instructions included therewith. The program instructions areexecutable by a device to cause the device to perform a method includingwriting different records to a correlation data structure correlatingdifferent data classifications of data to different queries. The methodfurther includes, subsequent to the writing, establishing acommunicative connection by a data analytics application to anunderlying database from over a computer communications network. Themethod also includes constructing in the data analytics application, adata model for data in the database and classifying the data in the datamodel. The method also includes selecting in the correlation datastructure, at least one of the different queries correlated to theclassification of the data in the data model. The method also includesdisplaying in the data analytics application, the selected one of thedifferent queries.

Implementations of the disclosure may include one or more of thefollowing optional features. In some configurations, the displayingincludes displaying a set of directives corresponding to the selectedone of the queries. In some examples, the directives are markup languagestatements. In some implementations the selection in the correlationdata structure, includes a selection of at least one of the differentqueries correlated to a combination of classifications of data in thedata model.

In some configurations, the selection in the correlation data structureincludes a selection of a user interface view that is a visualization ofa portion of the data model. In some examples, the selection in thecorrelation data structure includes a selection of a report of data froma portion of the data model. In some configurations the selection in thecorrelation data structure includes a selection of a programmaticdirective in a third-party application.

The details of one or more implementations of the disclosure are setforth in the accompanying drawings and the description below. Otheraspects, features, and advantages will be apparent from the descriptionand drawings, and from the claims.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute partof this specification, illustrate examples of the disclosure andtogether with the description, serve to explain the principles of thedisclosure. The examples illustrated herein are presently preferred, itbeing understood, however, that the disclosure is not limited to theprecise arrangements and instrumentalities shown, wherein:

FIG. 1 is pictorial illustration of a process for recommendingpre-constructed queries in data analytics;

FIG. 2 is a schematic illustration of a data analytics data processingsystem configured for recommending pre-constructed queries; and,

FIGS. 3A and 3B, taken together, are a flow chart illustrating a processfor recommending pre-constructed queries in data analytics.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Examples of the disclosure provide for recommending pre-constructedqueries in data analytics. In accordance with an example of thedisclosure, data in different data models for different databases may beclassified according to one or more classifications such as data type.Each of the classifications of corresponding classified data in the datamodel may then be associated in a correlation table with one or moredifferent data model queries or query blocks established for theclassified data. Thereafter, a data analytics application establishes acommunicative connection to an underlying database from over a computercommunications network and constructs a data model for data in thedatabase. As well, the data analytics application classifies the data inthe data model. Consequently, the data analytics application may thenselect in the correlation data structure at least one of the differentqueries correlated to the classification of the data in the data modelso as to display in the data analytics application to an end user, anintelligent recommendation for adding the selected one of the differentqueries to a set of queries to be executed against the data in the datamodel.

In further illustration, FIG. 1 pictorially shows a process forrecommending pre-constructed queries in data analytics. As shown in FIG.1 , each directive 140 applied in connection with data in a differentone of data models 120 produced from a corresponding data store 110 maybe correlated with a characterization 150 of the data, such as data typeof the data, or class of the data in terms of to which topic the datarelates, or a source of the data in terms of which portion of anorganization produced the data, to name only a few examples. Eachdirective 140, in turn, can be a simple query, a complex query, a set ofqueries, markup language representative of one or more queries, e.g. aquery block, a user interface view that is a visualization of a portionof corresponding one of the data models 120, a report of data from aportion of a corresponding one of the data models 120, or a programmaticdirective in a third-party application.

The correlations are then stored in a correlation data structure 130such as a table, list or flat file document, to name three examples.Thereafter, with respect to a contemporaneous data model 170 generatedfrom a database 160, the data in the data model 170 may be characterizedfor cross-reference with the characterizations 150 of the correlationdata structure 130 in order to identify a matching entry in thecorrelation data structure 130. By matching, while a complete match maybe preferred, it is to be recognized that a partial match beyond athreshold amount may be considered matching. In particular, to theextent that a combination of data each of different classificationpartially matches a single entry in the correlation data structure 130which entry consists of a combination of characterizations of dataspecified in connection with a previously asserted directive, athreshold number of matching characterizations may be consideredmatching.

In any event, corresponding directive 190 for the matching entry maythen be retrieved and subsequently proposed for use in respect to thecontemporaneous data model 170 in a user interface prompt 180 of a BItool. In this way, one or more customized enhancements to the BI toolmay be discovered on behalf of the end user so as to achieve the desired“actionable insights” into the data model 170 without requiring the enduser to master the skill set necessary to create a block of queriessufficient to achieve such “actionable insights”.

The process described in connection with FIG. 1 may be implemented in adata analytics data processing system. In further illustration, FIG. 2schematically shows a data analytics data processing system configuredfor recommending pre-constructed queries. The system includes a hostcomputing platform 210 that includes one or more computers each withmemory 220 and at least one processor 230. The host computing platform210 includes fixed storage 240 and is coupled to different remotecomputing devices 260 from over computer communications network 200,each with its own database 270. A data analytics application 250executes within the host computing platform 210 and provides a userinterface to conduct data analytics operations against different datamodels 280 stored within the fixed storage 240 of the host computingplatform 210. Finally, the system includes a recommendation enginemodule 300 coupled to the data analytics application 250.

The recommendation engine module 300 includes computer programinstructions that, when executing in the memory 220 of the hostcomputing platform 210, are enabled to monitor directives issued againstthe data models 280. The program instructions are further enabled toidentify, for each of the directives, data implicated by thecorresponding directive. The program instructions yet further areenabled to characterize the data and to create a record for eachdirective in a correlation table 290 correlating the directive with thecorresponding characterization or a corresponding combination ofcharacterizations of multiple data implicated by the directive.

Finally, the program instructions are enabled to process a newlygenerated one of the data models 280 for a corresponding one of thedatabases 270 by characterizing the data in the newly generated one ofthe data models 280, and also combinations of the data in the newlygenerated one of the data models 280, and to locate in the correlationtable 290 matching entries for selected ones of the characterizations.For each matching entry in the correlation table, the programinstructions are enabled to retrieve a corresponding directive and topresent a user interface prompt in the data analytics application 250 toadd the corresponding directive as an enhancement to the data analyticsapplication 250.

In even yet further illustration of the operation of the recommendationengine module 300, FIGS. 3A and 3B, taken together, are a flow chartillustrating a process for recommending pre-constructed queries in dataanalytics. Beginning in block 305, a query issued against a data modelmay be captured for analysis and in block 310, the data implicated bythe query may be characterized, for instance according to data type orclass of data, or source of data. Then, in block 315 the correlationtable may be inspected to determine whether or not an entry alreadyexists in the correlation table for the characterization. In decisionblock 320, if it is determined that an entry does not already exist inthe correlation table for the characterization, in block 325 an entry isadded to the correlation table for the characterization correlating thecharacterization to the directive. The process then repeats for the nextmonitored query.

Turning now to FIG. 3B, in block 330 a connection can be establishedbetween the data analytics application and a new data source from whicha data model may then be generated in block 335. Then, in block 340 datafrom the data model may be retrieved for processing and in block 345,characterized, either individually by column, or as a permutation ofcolumn headings in block 350. Thereafter, in block 355, a firstcharacterization may be selected and in block 360, the correlation tablemay be inspected for a matching entry. In decision block 365, if amatching entry can be found, in block 370, a corresponding directive forthe matching entry may be added to a list of recommendations. Indecision block 375, if additional characterizations remain to beprocessed, in block 385 a next characterization may be retrieved and theprocess continues through block 360. When no further characterizationsremain in decision block 375, the recommendations in the list may bepresented in a user interface dialog as suggested enhancements to thedata analytics application.

The present disclosure may be included within a system, a method, acomputer program product or any combination thereof. The computerprogram product may include a computer readable storage medium or mediahaving computer readable program instructions thereon for causing aprocessor to carry out aspects of the present disclosure. The computerreadable storage medium can be a tangible device that can retain andstore instructions for use by an instruction execution device. Thecomputer readable storage medium may be, for example, but is not limitedto, an electronic storage device, a magnetic storage device, an opticalstorage device, an electromagnetic storage device, a semiconductorstorage device, or any suitable combination of the foregoing.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network. The computer readable program instructions mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. Aspects of the present disclosure are described herein withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems), and computer program products according to examplesof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein includes anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousexamples of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which includes one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Finally, the terminology used herein is for the purpose of describingparticular examples only and is not intended to be limiting of thedisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“includes” and/or “including,” when used in this specification, specifythe presence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description; but, is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theexample was chosen and described in order to best explain the principlesof the disclosure and the practical application, and to enable others ofordinary skill in the art to understand the disclosure for variousexamples with various modifications as are suited to the particular usecontemplated.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. Accordingly, otherimplementations are within the scope of the following claims.

What is claimed is:
 1. A computer-implemented method executed by dataprocessing hardware that causes the data processing hardware to performoperations comprising: obtaining a plurality of directives, eachdirective of the plurality of directives corresponding to respectivedata from a respective database; for each respective directive of theplurality of directives, determining a respective classification of therespective data from the respective database corresponding to therespective directive; constructing, using a data analytics application,a data model for underlying data in an underlying database; determining,based on the data model, a classification of the underlying data;determining that the classification of at least one of the plurality ofdirectives matches the classification of the underlying data; and basedon determining that the classification of at least one of the pluralityof directives matches the classification of the underlying data,displaying, to a user, the at least one of the plurality of directives.2. The method of claim 1, wherein each directive of the plurality ofdirectives comprises a query.
 3. The method of claim 1, wherein eachdirective of the plurality of directives comprises a markup languagestatement.
 4. The method of claim 1, wherein the operations furthercomprise, for each of the plurality of directives, determining arespective correlation of the respective directive with the respectiveclassification of the respective data from the respective databasecorresponding to the respective directive.
 5. The method of claim 4,wherein the operations further comprise, for each of the plurality ofdirectives, adding, to a correlation data structure, the respectivecorrelation of the respective directive with the respectiveclassification of the respective data from the respective databasecorresponding to the respective directive.
 6. The method of claim 5,wherein determining that the classification of the at least one of theplurality of directives matches the classification of the underlyingdata comprises selecting, in the correlation data structure, the atleast one of the plurality of directives based on the classification ofthe underlying data matching the respective classification of the atleast one of the plurality of directives.
 7. The method of claim 6,wherein selecting the at least one of the plurality of directivescomprises selecting the at least one of the directives correlated to acombination of classifications of data in the data model.
 8. The methodof claim 6, wherein selecting the at least one of the plurality ofdirectives comprises selecting a user interface view that is avisualization of a portion of the data model.
 9. The method of claim 6,wherein selecting the at least one of the plurality of directivescomprises selecting of a report of data from a portion of the datamodel.
 10. The method of claim 1, wherein determining that theclassification of the at least one of the plurality of directivesmatches the classification of the underlying data comprises: determiningthat the classification of the at least one of the plurality ofdirectives is a partial match to the classification of the underlying;and determining that the partial match satisfies a threshold.
 11. Asystem comprising: data processing hardware; and memory hardware incommunication with the data processing hardware, the memory hardwarestoring instructions that when executed on the data processing hardwarecause the data processing hardware to perform operations comprising:obtaining a plurality of directives, each directive of the plurality ofdirectives corresponding to respective data from a respective database;for each respective directive of the plurality of directives,determining a respective classification of the respective data from therespective database corresponding to the respective directive;constructing, using a data analytics application, a data model forunderlying data in an underlying database; determining, based on thedata model, a classification of the underlying data; determining thatthe classification of at least one of the plurality of directivesmatches the classification of the underlying data; and based ondetermining that the classification of at least one of the plurality ofdirectives matches the classification of the underlying data,displaying, to a user, the at least one of the plurality of directives.12. The system of claim 11, wherein each directive of the plurality ofdirectives comprises a query.
 13. The system of claim 11, wherein eachdirective of the plurality of directives comprises a markup languagestatement.
 14. The system of claim 11, wherein the operations furthercomprise, for each of the plurality of directives, determining arespective correlation of the respective directive with the respectiveclassification of the respective data from the respective databasecorresponding to the respective directive.
 15. The system of claim 14,wherein the operations further comprise, for each of the plurality ofdirectives, adding, to a correlation data structure, the respectivecorrelation of the respective directive with the respectiveclassification of the respective data from the respective databasecorresponding to the respective directive.
 16. The system of claim 15,wherein determining that the classification of the at least one of theplurality of directives matches the classification of the underlyingdata comprises selecting, in the correlation data structure, the atleast one of the plurality of directives based on the classification ofthe underlying data matching the respective classification of the atleast one of the plurality of directives.
 17. The system of claim 16,wherein selecting the at least one of the plurality of directivescomprises selecting the at least one of the directives correlated to acombination of classifications of data in the data model.
 18. The systemof claim 16, wherein selecting the at least one of the plurality ofdirectives comprises selecting a user interface view that is avisualization of a portion of the data model.
 19. The system of claim16, wherein selecting the at least one of the plurality of directivescomprises selecting of a report of data from a portion of the datamodel.
 20. The system of claim 11, wherein determining that theclassification of the at least one of the plurality of directivesmatches the classification of the underlying data comprises: determiningthat the classification of the at least one of the plurality ofdirectives is a partial match to the classification of the underlying;and determining that the partial match satisfies a threshold.